CASSINI is a network-aware job scheduler designed specifically for machine learning (ML) clusters. It introduces a novel geometric abstraction that takes into consideration the communication patterns of different jobs when placing them on network links. This is achieved through the use of an affinity graph, which identifies time-shift values to adjust the communication phases of a subset of jobs. By doing so, CASSINI ensures that the communication patterns of jobs sharing the same network link are interleaved with each other. In order to evaluate its performance, experiments were conducted using 13 common ML models on a 24-server testbed. The results showed that compared to state-of-the-art ML schedulers, CASSINI significantly improves both the average and tail completion time of jobs by up to 1.6 times and 2.5 times, respectively. Additionally, CASSINI was found to reduce the number of ECN marked packets in the cluster by up to 33 times. The expanded context provides further insights into parallelism techniques used in DNN training systems such as model parallelism, tensor parallelism, and hybrid data/pipeline/tensor parallelism. These techniques involve partitioning models horizontally or vertically across multiple servers and distributing tensors or data across workers. The communication patterns during different phases of these parallelization approaches are also discussed. Overall, CASSINI's ability to optimize job scheduling based on network-awareness and its demonstrated improvements in completion time and packet reduction make it a valuable tool for efficient machine learning cluster management.
CASSINI is a special computer program that helps organize tasks for machine learning computers. It uses a smart way to think about how the computers talk to each other when deciding what tasks to do. It also makes sure that tasks are done at the same time on the same computer link. Tests showed that CASSINI made things faster and used less data than other programs. The text also talks about different ways to make computers work together, like splitting up big tasks or dividing up the data they use. Overall, CASSINI makes machine learning computers work better by being aware of how they communicate and making things more efficient.
Definitions
- Network-aware: Knowing how computers communicate with each other.
- Job scheduler: A program that decides which tasks should be done and when.
- Machine learning: Computers learning from data and getting smarter over time.
- Clusters: Groups of computers working together as a team.
- Geometric abstraction: A clever way of thinking about something using shapes and patterns.
- Affinity graph: A tool used to make sure certain tasks are done together on the same computer link.
- Completion time: How long it takes for a task to finish.
- Packet reduction: Using less data when sending information between computers.
- State-of-the-art schedulers: The best and most advanced job scheduling programs available.
- Parallelism techniques: Different ways of making multiple computers work together at the same time.
- DNN training systems: Special systems for training deep neural networks, which are types
Introducing CASSINI: A Network-Aware Job Scheduler for Machine Learning Clusters
In recent years, machine learning (ML) has become an increasingly popular tool in many industries. As the demand for ML applications grows, so does the need for efficient and effective management of ML clusters. To address this challenge, researchers have developed a new network-aware job scheduler called CASSINI that is designed specifically for ML clusters. This article will discuss how CASSINI works and explain its performance advantages over existing state-of-the-art ML schedulers.
How Does CASSINI Work?
CASSINI introduces a novel geometric abstraction that takes into consideration the communication patterns of different jobs when placing them on network links. This is achieved through the use of an affinity graph, which identifies time-shift values to adjust the communication phases of a subset of jobs. By doing so, CASSINI ensures that the communication patterns of jobs sharing the same network link are interleaved with each other.
Evaluating Performance with Experiments
To evaluate its performance, experiments were conducted using 13 common ML models on a 24-server testbed. The results showed that compared to state-of-the-art ML schedulers, CASSINI significantly improves both average and tail completion times by up to 1.6 times and 2.5 times respectively as well as reduces ECN marked packets in the cluster by up to 33 times.
Parallelism Techniques Used in DNN Training Systems
The expanded context provides further insights into parallelism techniques used in deep neural networks (DNNs) training systems such as model parallelism, tensor parallelism and hybrid data/pipeline/tensor parallelism. These techniques involve partitioning models horizontally or vertically across multiple servers and distributing tensors or data across workers while considering their communication patterns during different phases of these approaches..
Conclusion
Overall, CASSINI's ability to optimize job scheduling based on network awareness and its demonstrated improvements in completion time and packet reduction make it a valuable tool for efficient machine learning cluster management
We present CASSINI, a network-aware job scheduler for machine learning (ML) clusters. CASSINI introduces a novel geometric abstraction to consider the communication pattern of different jobs while placing them on network links. To do so, CASSINI uses an affinity graph that finds a series of time-shift values to adjust the communication phases of a subset of jobs, such that the communication patterns of jobs sharing the same network link are interleaved with each other. Experiments with 13 common ML models on a 24-server testbed demonstrate that compared to the state-of-the-art ML schedulers, CASSINI improves the average and tail completion time of jobs by up to 1.6x and 2.5x, respectively. Moreover, we show that CASSINI reduces the number of ECN marked packets in the cluster by up to 33x.
